Heuristic Stochastic Stope Layout Optimization

• Author / Creator

  Wilson, Brandon

• Stope layout optimization is a critical process for the evaluation and optimization of underground mining operations. Conventionally, stopes layouts are either planned manually or optimized using deterministic methods. Developing stochastic approaches to stope optimization is valuable due to the impact of uncertain inputs on optimization results, the non-linear nature of objective functions common to mining, and the large number of variables that impact the results. Deterministic methods are not able to account for non-linearities and produce sub-optimal solutions when the inputs are averaged before the transfer function response is calculated.
  In this thesis, an existing two-stage heuristic algorithm that greedily selects strips from a deterministic block model is modified to consider a set of realizations to account for uncertainty in the subsurface and non-linearities in the transfer function. As an example implementation, the algorithm is applied to a set of synthetic copper data with a non-linear recovery function and compared to the results from a deterministic stope optimization, showing an improvement of 4.90% in value.
  A full case study is produced using the algorithm for a real-world vein-like deposit, showing the ability for risk evaluation and the improved calculation of non-linear objectives with a 17% increase in value when calculation over realizations is considered. After optimizing layouts for a variety of risk discounts, stochastic dominance is utilized to reduce the field of possible solutions. The layouts are scheduled, and an overall solution is determined from the resulting net present value distributions.

• Subjects / Keywords

  • Heuristics
  • Risk
  • Stope Optimization
  • Decision Making

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-77bw-8d18

• License

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